Iterative Maximum Likelihood Algorithms

Iterative Maximum Likelihood Algorithms is a computational methodology within statistical inference that iteratively refines parameter estimates to maximize the likelihood function through successive approximations, distinguishing itself from closed-form solutions by its reliance on stepwise convergence to the optimal estimate.

367

Publications

29K

Citations

859

Authors

418

Institutions

Publications per year

2017–2026

32

Authors

859

  1. AH
    Albert H. Moore

    U.S. Air Force Institute of Technology, United States

    7 publications · 588 citations

    7

    h-index

  2. HL
    H. Leon Harter

    Wright-Patterson Air Force Base, United States

    6 publications · 576 citations

    6

    h-index

  3. KL
    Kenneth Lange

    University of California, Los Angeles, United States

    5 publications · 2.1K citations

    5

    h-index

  4. AW
    A.J. Weiss

    Tel Aviv University, Israel

    4 publications · 359 citations

    4

    h-index

  5. AF
    Andreas F. Molisch

    University of Southern California, United States

    4 publications · 176 citations

    4

    h-index

1–5 of 859

Venues

  1. IEEE Transactions on Signal Processing

    28 publications · 1.5K citations

    22

    h-index

  2. Journal of the American Statistical Association

    21 publications · 4.7K citations

    20

    h-index

  3. Technometrics

    10 publications · 1.3K citations

    10

    h-index

  4. IEEE Transactions on Medical Imaging

    10 publications · 1K citations

    10

    h-index

  5. IEEE Transactions on Nuclear Science

    9 publications · 588 citations

    9

    h-index

1–5